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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2021.3116793
|2 doi
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|a pubmed24n1105.xml
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|a DE-627
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|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Zhai, Yingjie
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Bifurcated Backbone Strategy for RGB-D Salient Object Detection
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|c 2021
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|a Text
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|a Date Completed 27.10.2021
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|a Date Revised 27.10.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Multi-level feature fusion is a fundamental topic in computer vision. It has been exploited to detect, segment and classify objects at various scales. When multi-level features meet multi-modal cues, the optimal feature aggregation and multi-modal learning strategy become a hot potato. In this paper, we leverage the inherent multi-modal and multi-level nature of RGB-D salient object detection to devise a novel Bifurcated Backbone Strategy Network (BBS-Net). Our architecture, is simple, efficient, and backbone-independent. In particular, first, we propose to regroup the multi-level features into teacher and student features using a bifurcated backbone strategy (BBS). Second, we introduce a depth-enhanced module (DEM) to excavate informative depth cues from the channel and spatial views. Then, RGB and depth modalities are fused in a complementary way. Extensive experiments show that BBS-Net significantly outperforms 18 state-of-the-art (SOTA) models on eight challenging datasets under five evaluation measures, demonstrating the superiority of our approach (~4% improvement in S-measure vs . the top-ranked model: DMRA). In addition, we provide a comprehensive analysis on the generalization ability of different RGB-D datasets and provide a powerful training set for future research. The complete algorithm, benchmark results, and post-processing toolbox are publicly available at https://github.com/zyjwuyan/BBS-Net
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|a Journal Article
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| 700 |
1 |
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|a Fan, Deng-Ping
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yang, Jufeng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Borji, Ali
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Shao, Ling
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Han, Junwei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Liang
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 30(2021) vom: 15., Seite 8727-8742
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|x 1941-0042
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|g volume:30
|g year:2021
|g day:15
|g pages:8727-8742
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|u http://dx.doi.org/10.1109/TIP.2021.3116793
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